system

Abstract

The system according to this embodiment aims to automatically generate original lyrics and melodies that match a theme or mood specified by the user. [Solution] The system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives a theme or mood from the user. The generation unit analyzes the theme or mood specified by the reception unit and generates lyrics and a melody. The provision unit provides the lyrics and melody generated by the generation unit to the user.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

[0007] The system according to this embodiment can automatically generate original lyrics and melodies that match a theme or mood specified by the user. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The music generation system according to an embodiment of the present invention is a system that uses advanced generation AI to automatically generate original lyrics and melodies that match a theme or mood specified by the user. The music generation system generates and provides lyrics and melodies based on the theme or mood specified by the user. For example, the user specifies a theme or mood. In this case, the user inputs a theme or mood such as "happy," "sad," or "love." For example, the user specifies the theme "happy summer memories." This information is input to the generation AI. Next, the generation AI analyzes the input theme or mood and generates lyrics and melodies based on it. The generation AI has learned from past music data and lyric data and generates lyrics and melodies that match the specified theme or mood. For example, based on the theme "happy summer memories," bright and cheerful lyrics and melodies are generated. The generated lyrics and melody are provided to the user. The user can check the generated lyrics and melody and make corrections or adjustments as needed. For example, the user can change part of the generated lyrics or adjust the tempo of the melody. This mechanism allows the user to easily create original lyrics and melodies. By using a generation AI, it's possible to automatically generate music that matches the theme and mood specified by the user, significantly reducing the effort involved in music production. Furthermore, the generated music reflects the user's personality and emotions, allowing for the creation of more personalized songs. For example, if a user wants to create a song with the theme of "love," the generation AI will generate lyrics and a melody related to "love." This allows users to easily create music that expresses their feelings and thoughts. Additionally, the generated music can be customized to the user's preferences, resulting in a more satisfying experience. In short, the music generation system can automatically generate and provide original lyrics and melodies that match the user's specified theme and mood.

[0029] The music generation system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives a theme or mood from the user. When the user specifies a theme or mood, they can input themes or moods such as "happy," "sad," or "love." For example, the user specifies the theme "happy summer memories." This information is input to the generation AI. The generation unit uses the generation AI to analyze the theme or mood specified by the reception unit and generates lyrics and a melody. The generation AI has learned from past music data and lyric data and generates lyrics and a melody that match the specified theme or mood. For example, the generation unit generates bright and cheerful lyrics and a melody based on the theme "happy summer memories." The generation unit can also use the generation AI to generate lyrics and a melody based on a specified theme or mood. For example, the generation unit uses the generation AI to generate lyrics and a melody with "love" as the theme. The provision unit provides the user with the lyrics and melody generated by the generation unit. The provision unit allows the user to review the generated lyrics and melody and make corrections or adjustments as needed. For example, the provision unit can change some of the generated lyrics or adjust the tempo of the melody. As a result, the music generation system according to this embodiment can automatically generate and provide original lyrics and melodies that match the theme or mood specified by the user.

[0030] The reception desk allows users to specify themes and moods. Users can input themes and moods such as "fun," "sad," or "love." Specifically, users select themes and moods using text boxes and dropdown menus through a dedicated interface. This interface is intuitive and user-friendly, designed to allow users to easily input themes and moods. For example, if a user specifies the theme "fun summer memories," they would select the "fun" mood on the interface and input "summer memories" into the text box. This information is then fed into the generation AI. The generation AI analyzes the user's specified theme and mood and uses it as foundational data to generate appropriate lyrics and melodies. The reception desk receives user input in real time and quickly transfers it to the generation desk. This minimizes the time between the user specifying a theme or mood and the song being generated. The reception desk also saves the user's input history, allowing for the reuse of previously specified themes and moods. This enables users to generate new songs based on previously generated tracks, creating more consistent works. Furthermore, the reception desk also has a function to provide suggestions related to the theme and mood specified by the user. For example, for a user who specifies the theme "fun summer memories," it will suggest related keywords such as "beach," "barbecue," and "fireworks," supporting more specific theme settings. This allows users to specify more detailed and specific themes and moods, improving the quality of the generated music.

[0031] The generation unit uses a generation AI to analyze the theme and mood specified by the reception unit and generate lyrics and melodies. The generation AI has learned from past song and lyric data and generates lyrics and melodies that match the specified theme and mood. Specifically, the generation AI uses natural language processing technology to analyze the theme and mood specified by the user and generates appropriate lyrics based on that. For example, based on the theme "fun summer memories," the generation AI generates bright and cheerful lyrics. The generation AI has learned the structure, rhythm, and melody line of songs based on past song data and can generate melodies that match the specified theme and mood. For example, the generation unit generates a bright and cheerful melody based on the theme "fun summer memories." The generation unit can also use the generation AI to generate lyrics and melodies based on a specified theme and mood. For example, the generation unit generates lyrics and a melody with the theme of "love." The generation AI can generate multiple variations based on the theme and mood specified by the user. This allows the user to choose their favorite lyrics and melody from multiple options. Furthermore, the generation unit provides the generated lyrics and melody to the user in real time, allowing the user to check them immediately. This allows users to immediately review the generated music and make corrections or adjustments as needed. The generation unit regularly updates the AI's training data and incorporates new music and lyric data, enabling it to consistently generate music that reflects the latest trends and styles. As a result, the generation unit can provide high-quality music that meets the user's needs.

[0032] The provider unit provides the user with lyrics and melodies generated by the generator unit. The provider unit allows users to review the generated lyrics and melodies and make corrections or adjustments as needed. Specifically, the provider unit provides an interface for users to modify parts of the generated lyrics or adjust the tempo of the melody. This interface is intuitive and easy to use, allowing users to easily make corrections and adjustments. For example, if a user wants to change part of the generated lyrics, they can select the relevant section on the provider unit's interface and enter new lyrics. Similarly, if they want to adjust the tempo of the melody, they can use a slider to change the tempo. The provider unit reflects user corrections and adjustments in real time, allowing for immediate confirmation. This enables users to customize the generated song to their liking. Furthermore, the provider unit has a function to save the generated song, allowing users to access it again later. This allows users to review the generated song at any time and make further corrections or adjustments as needed. The provider unit also provides a function for sharing the generated song. For example, users can share the generated song via social media or email. This allows users to share their creations with others and receive feedback. The service provider can collect user feedback and incorporate it into the generation and reception departments, thereby improving the entire system. This allows the service provider to offer users a high-quality music generation experience and increase user satisfaction.

[0033] The generation unit learns from past song data and lyric data. For example, the generation unit uses past song data and lyric data to generate lyrics and melodies that match a specified theme or mood. For example, the generation unit's generation AI learns from past song data and generates lyrics and melodies based on a specified theme or mood. Alternatively, the generation unit can learn from past lyric data and generate lyrics and melodies based on a specified theme or mood. This allows the generation unit to generate more accurate lyrics and melodies by learning from past data. Some or all of the above processing in the generation unit may be performed using the generation AI, or it may be performed without the generation AI. For example, the generation unit can input past song data and lyric data into the generation AI, and the generation AI can generate lyrics and melodies based on the data it has learned.

[0034] The generation unit generates lyrics and melodies based on a specified theme or mood. For example, the generation unit generates lyrics and melodies based on a theme or mood specified by the user. For example, the generation unit's generation AI generates bright and cheerful lyrics and melodies based on the theme of "fun summer memories." The generation unit can also have its generation AI generate lyrics and melodies with the theme of "love." In this way, the generation unit can generate lyrics and melodies that match the specified theme or mood. Some or all of the above processing in the generation unit may be performed using the generation AI, or it may be performed without using the generation AI. For example, the generation unit can input a specified theme or mood into the generation AI, and the generation AI can generate lyrics and melodies.

[0035] The providing unit can review the lyrics and melody generated by the user and make corrections or adjustments. For example, the providing unit can review the lyrics and melody generated by the user and make corrections or adjustments as needed. For example, the providing unit can change parts of the generated lyrics or adjust the tempo of the melody. The providing unit can also provide the generated lyrics and melody to the user and make corrections or adjustments after the user has reviewed them. This allows the providing unit to review the generated lyrics and melody and make corrections or adjustments as needed. Some or all of the above processing in the providing unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the providing unit can input the generated lyrics and melody into a generation AI, which can then make corrections or adjustments.

[0036] The reception desk can analyze the user's past theme and mood selection history and automatically suggest the most suitable theme and mood. For example, the reception desk can suggest similar themes and moods based on themes and moods the user has selected in the past. It can also suggest seasonally appropriate themes and moods based on themes and moods the user has selected during a particular season. Furthermore, the reception desk can suggest themes and moods suitable for a specific time of day based on the user's past selection history. This allows the reception desk to suggest the most suitable theme and mood based on the user's past selection history. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception desk can input the user's past selection history data into a generative AI, which can then suggest the most suitable theme and mood.

[0037] The reception desk can automatically complete keywords related to the theme or mood specified by the user. For example, if the user specifies "love" as the theme, the reception desk can automatically complete related keywords such as "romance," "passion," and "bond." Similarly, if the user specifies "summer" as the theme, the reception desk can automatically complete related keywords such as "beach," "sun," and "vacation." Furthermore, if the user specifies "sad" as the mood, the reception desk can automatically complete related keywords such as "tears," "heartbreak," and "loneliness." In this way, the reception desk can set more specific themes and moods by automatically completing keywords related to the theme or mood specified by the user. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception desk can input the theme or mood specified by the user into a generative AI, and the generative AI can automatically complete related keywords.

[0038] The reception desk can suggest region-specific themes and moods, taking into account the user's geographical location. For example, if the user is at the beach, the reception desk can suggest sea-related themes and moods. If the user is in a mountainous area, the reception desk can suggest nature and adventure-related themes and moods. Furthermore, if the user is in an urban area, the reception desk can suggest urban themes and moods. In this way, the reception desk can suggest region-specific themes and moods based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception desk can input the user's geographical location information into a generative AI, which can then suggest region-specific themes and moods.

[0039] The reception desk can analyze a user's social media activity and suggest relevant themes and moods. For example, if a user frequently posts about "travel" on social media, the reception desk can suggest travel-related themes and moods. Similarly, if a user frequently posts about "music," the reception desk can suggest music-related themes and moods. Furthermore, if a user frequently posts about "cooking," the reception desk can suggest cooking-related themes and moods. In this way, the reception desk can suggest relevant themes and moods based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input the user's social media activity data into a generative AI, which can then suggest relevant themes and moods.

[0040] The generation unit can generate more personalized lyrics and melodies by referring to the user's past song generation history during the generation process. For example, the generation unit can generate lyrics and melodies in a similar style based on the style of songs the user has previously generated. It can also generate relevant lyrics and melodies based on themes and moods the user has previously preferred. Furthermore, the generation unit can generate lyrics and melodies influenced by specific artists from the user's past song generation history. In this way, the generation unit can generate more personalized lyrics and melodies based on the user's past song generation history. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's past song generation history data into a generation AI, which can then generate more personalized lyrics and melodies.

[0041] The generation unit can incorporate cultural backgrounds and historical elements related to the specified theme or mood during the generation process. For example, if the user specifies "love" as the theme, the generation unit can generate lyrics and melodies that incorporate elements of historical love songs. Similarly, if the user specifies "summer" as the theme, the generation unit can generate lyrics and melodies that incorporate cultural elements related to summer traditions. Furthermore, if the user specifies "sad" as the mood, the generation unit can generate lyrics and melodies that incorporate elements of tragic stories and poems. In this way, the generation unit can generate more profound lyrics and melodies by incorporating cultural backgrounds and historical elements related to the specified theme or mood. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the specified theme or mood into a generation AI, which can then generate lyrics and melodies that incorporate cultural backgrounds and historical elements.

[0042] The generation unit can incorporate region-specific musical styles by considering the user's geographical location during the generation process. For example, if the user is in Latin America, the generation unit can generate lyrics and melodies incorporating elements of Latin music. If the user is in Africa, the generation unit can generate lyrics and melodies incorporating rhythms and melodies of African music. Furthermore, if the user is in Asia, the generation unit can generate lyrics and melodies incorporating elements of traditional Asian music. In this way, the generation unit can generate more locally rooted songs by incorporating region-specific musical styles based on the user's geographical location. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI, which can then generate lyrics and melodies incorporating region-specific musical styles.

[0043] The generation unit can analyze the user's social media activity during generation and generate lyrics and melodies that reflect relevant trends. For example, if the user frequently posts about "travel" on social media, the generation unit can generate travel-related lyrics and melodies. Similarly, if the user frequently posts about "music," the generation unit can generate music-related lyrics and melodies that reflect relevant trends. Furthermore, if the user frequently posts about "cooking," the generation unit can generate cooking-related lyrics and melodies. In this way, the generation unit can generate lyrics and melodies that reflect relevant trends based on the user's social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's social media activity data into a generation AI, which can then generate lyrics and melodies that reflect relevant trends.

[0044] The service provider can, at the time of delivery, refer to the user's past revision history to make optimal revision suggestions. For example, the service provider can make similar revision suggestions based on parts the user has previously revised. Furthermore, the service provider can make revision suggestions that match a specific style based on the user's revision history. In addition, the service provider can make revision suggestions influenced by a specific artist based on the user's past revision history. This allows the service provider to make optimal revision suggestions based on the user's past revision history. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input the user's past revision history data into a generative AI, which can then make optimal revision suggestions.

[0045] The service provider can add a function to collect user feedback in real time during the service provision process and reflect it in the next generation. For example, the service provider can provide an interface that allows users to provide feedback in real time. The service provider can also introduce an algorithm that reflects user feedback in the next generation. Furthermore, the service provider can build a system that collects user feedback in real time and reflects it in the generation AI. This allows the service provider to collect user feedback in real time and reflect it in the next generation, thereby enabling the creation of music that better meets user needs. Some or all of the above processing in the service provider may be performed using the generation AI or not. For example, the service provider can input user feedback data into the generation AI, which can then reflect it in the next generation.

[0046] The service provider can, at the time of delivery, consider the user's geographical location and make region-specific modification suggestions. For example, if the user is at the beach, the service provider can make modification suggestions related to the sea. If the user is in a mountainous area, the service provider can make modification suggestions related to nature or adventure. Furthermore, if the user is in an urban area, the service provider can make urban modification suggestions. In this way, the service provider can make more region-specific modifications by making region-specific modification suggestions based on the user's geographical location. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input the user's geographical location information into a generative AI, and the generative AI can make region-specific modification suggestions.

[0047] The service provider can analyze the user's social media activity at the time of delivery and make correction suggestions that reflect relevant feedback. For example, if the user frequently posts about "travel" on social media, the service provider can make travel-related correction suggestions. Similarly, if the user frequently posts about "music," the service provider can make music-related correction suggestions. Furthermore, if the user frequently posts about "cooking," the service provider can make cooking-related correction suggestions. This allows the service provider to make more appropriate corrections by making correction suggestions that reflect relevant feedback based on the user's social media activity. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input the user's social media activity data into a generative AI, which can then make correction suggestions that reflect relevant feedback.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The reception desk can automatically suggest relevant images and videos based on the theme or mood specified by the user. For example, if the user specifies the theme "fun summer memories," the reception desk will suggest images and videos of beaches and summer festivals. If the user specifies the theme "love," it can suggest images and videos of romantic scenes. Furthermore, if the user specifies the mood "sad," it can suggest touching movie scenes or poetic images. In this way, the reception desk can provide visual content related to the theme or mood specified by the user, allowing them to form a more concrete image.

[0050] The service provider can provide a function that allows users to receive real-time feedback from other users when reviewing the lyrics and melody they have generated. For example, a user can share a song they have generated and receive comments and ratings from other users in real time. The service provider can also suggest revisions and adjustments to the song based on the feedback from other users. Furthermore, the service provider can compile the feedback and present the revision that received the most support from users. This allows the service provider to help users improve their songs while receiving feedback from other users.

[0051] The reception desk can automatically suggest relevant literary works and poems based on themes and moods specified by the user. For example, if the user specifies "love" as their theme, it can suggest Shakespearean sonnets and romantic poems. If the user specifies "sad" as their mood, it can suggest moving poems and stories. Furthermore, if the user specifies "adventure" as their theme, it can suggest adventure novels and poems. In this way, the reception desk can gain deeper inspiration by providing literary works and poems related to the themes and moods specified by the user.

[0052] The service provider can include a function to add visual effects when users review the generated lyrics and melody. For example, if a user specifies a cheerful theme, bright colors and dynamic effects can be added. If a user specifies a sad mood, calm colors and quiet effects can be added. Furthermore, if a user specifies a relaxed mood, gentle colors and mellow effects can be added. This allows the service provider to visually enjoy the music generated by the user.

[0053] The reception desk can automatically suggest relevant artworks and photographs based on the theme or mood specified by the user. For example, if the user specifies "love" as their theme, it will suggest romantic paintings and photographs. If the user specifies "sadness" as their mood, it can suggest emotionally moving artworks and photographs. Furthermore, if the user specifies "adventure" as their theme, it can suggest artworks and photographs depicting adventurous scenes. In this way, the reception desk can provide artworks and photographs related to the theme or mood specified by the user, allowing them to form a more concrete image.

[0054] The service provider can include a feature that uses a voice assistant to guide users as they review the generated lyrics and melody. For example, if a user wants to change part of a song, the voice assistant can guide them through the specific steps of making the change. Similarly, if a user wants to adjust the tempo of a melody, the voice assistant can suggest an appropriate tempo. Furthermore, if a user wants to revise part of the lyrics, the voice assistant can offer revision suggestions. This allows the service provider to enable users to easily revise songs with the guidance of a voice assistant.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The reception desk allows users to specify a theme or mood. Users can enter themes or moods such as "fun," "sad," or "love." For example, they can specify the theme "fun summer memories." Step 2: The generation unit analyzes the theme and mood specified by the reception unit and generates lyrics and melody. The generation unit uses a generation AI to learn from past song data and lyric data and generates lyrics and melody that match the specified theme and mood. For example, based on the theme "fun summer memories," it generates bright and cheerful lyrics and melody. Step 3: The providing unit provides the user with the lyrics and melody generated by the generating unit. The user can review the generated lyrics and melody and make corrections or adjustments as needed. For example, they can change part of the lyrics or adjust the tempo of the melody.

[0057] (Example of form 2) The music generation system according to an embodiment of the present invention is a system that uses advanced generation AI to automatically generate original lyrics and melodies that match a theme or mood specified by the user. The music generation system generates and provides lyrics and melodies based on the theme or mood specified by the user. For example, the user specifies a theme or mood. In this case, the user inputs a theme or mood such as "happy," "sad," or "love." For example, the user specifies the theme "happy summer memories." This information is input to the generation AI. Next, the generation AI analyzes the input theme or mood and generates lyrics and melodies based on it. The generation AI has learned from past music data and lyric data and generates lyrics and melodies that match the specified theme or mood. For example, based on the theme "happy summer memories," bright and cheerful lyrics and melodies are generated. The generated lyrics and melody are provided to the user. The user can check the generated lyrics and melody and make corrections or adjustments as needed. For example, the user can change part of the generated lyrics or adjust the tempo of the melody. This mechanism allows the user to easily create original lyrics and melodies. By using a generation AI, it's possible to automatically generate music that matches the theme and mood specified by the user, significantly reducing the effort involved in music production. Furthermore, the generated music reflects the user's personality and emotions, allowing for the creation of more personalized songs. For example, if a user wants to create a song with the theme of "love," the generation AI will generate lyrics and a melody related to "love." This allows users to easily create music that expresses their feelings and thoughts. Additionally, the generated music can be customized to the user's preferences, resulting in a more satisfying experience. In short, the music generation system can automatically generate and provide original lyrics and melodies that match the user's specified theme and mood.

[0058] The music generation system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives a theme or mood from the user. When the user specifies a theme or mood, they can input themes or moods such as "happy," "sad," or "love." For example, the user specifies the theme "happy summer memories." This information is input to the generation AI. The generation unit uses the generation AI to analyze the theme or mood specified by the reception unit and generates lyrics and a melody. The generation AI has learned from past music data and lyric data and generates lyrics and a melody that match the specified theme or mood. For example, the generation unit generates bright and cheerful lyrics and a melody based on the theme "happy summer memories." The generation unit can also use the generation AI to generate lyrics and a melody based on a specified theme or mood. For example, the generation unit uses the generation AI to generate lyrics and a melody with "love" as the theme. The provision unit provides the user with the lyrics and melody generated by the generation unit. The provision unit allows the user to review the generated lyrics and melody and make corrections or adjustments as needed. For example, the provision unit can change some of the generated lyrics or adjust the tempo of the melody. As a result, the music generation system according to this embodiment can automatically generate and provide original lyrics and melodies that match the theme or mood specified by the user.

[0059] The reception desk allows users to specify themes and moods. Users can input themes and moods such as "fun," "sad," or "love." Specifically, users select themes and moods using text boxes and dropdown menus through a dedicated interface. This interface is intuitive and user-friendly, designed to allow users to easily input themes and moods. For example, if a user specifies the theme "fun summer memories," they would select the "fun" mood on the interface and input "summer memories" into the text box. This information is then fed into the generation AI. The generation AI analyzes the user's specified theme and mood and uses it as foundational data to generate appropriate lyrics and melodies. The reception desk receives user input in real time and quickly transfers it to the generation desk. This minimizes the time between the user specifying a theme or mood and the song being generated. The reception desk also saves the user's input history, allowing for the reuse of previously specified themes and moods. This enables users to generate new songs based on previously generated tracks, creating more consistent works. Furthermore, the reception desk also has a function to provide suggestions related to the theme and mood specified by the user. For example, for a user who specifies the theme "fun summer memories," it will suggest related keywords such as "beach," "barbecue," and "fireworks," supporting more specific theme settings. This allows users to specify more detailed and specific themes and moods, improving the quality of the generated music.

[0060] The generation unit uses a generation AI to analyze the theme and mood specified by the reception unit and generate lyrics and melodies. The generation AI has learned from past song and lyric data and generates lyrics and melodies that match the specified theme and mood. Specifically, the generation AI uses natural language processing technology to analyze the theme and mood specified by the user and generates appropriate lyrics based on that. For example, based on the theme "fun summer memories," the generation AI generates bright and cheerful lyrics. The generation AI has learned the structure, rhythm, and melody line of songs based on past song data and can generate melodies that match the specified theme and mood. For example, the generation unit generates a bright and cheerful melody based on the theme "fun summer memories." The generation unit can also use the generation AI to generate lyrics and melodies based on a specified theme and mood. For example, the generation unit generates lyrics and a melody with the theme of "love." The generation AI can generate multiple variations based on the theme and mood specified by the user. This allows the user to choose their favorite lyrics and melody from multiple options. Furthermore, the generation unit provides the generated lyrics and melody to the user in real time, allowing the user to check them immediately. This allows users to immediately review the generated music and make corrections or adjustments as needed. The generation unit regularly updates the AI's training data and incorporates new music and lyric data, enabling it to consistently generate music that reflects the latest trends and styles. As a result, the generation unit can provide high-quality music that meets the user's needs.

[0061] The provider unit provides the user with lyrics and melodies generated by the generator unit. The provider unit allows users to review the generated lyrics and melodies and make corrections or adjustments as needed. Specifically, the provider unit provides an interface for users to modify parts of the generated lyrics or adjust the tempo of the melody. This interface is intuitive and easy to use, allowing users to easily make corrections and adjustments. For example, if a user wants to change part of the generated lyrics, they can select the relevant section on the provider unit's interface and enter new lyrics. Similarly, if they want to adjust the tempo of the melody, they can use a slider to change the tempo. The provider unit reflects user corrections and adjustments in real time, allowing for immediate confirmation. This enables users to customize the generated song to their liking. Furthermore, the provider unit has a function to save the generated song, allowing users to access it again later. This allows users to review the generated song at any time and make further corrections or adjustments as needed. The provider unit also provides a function for sharing the generated song. For example, users can share the generated song via social media or email. This allows users to share their creations with others and receive feedback. The service provider can collect user feedback and incorporate it into the generation and reception departments, thereby improving the entire system. This allows the service provider to offer users a high-quality music generation experience and increase user satisfaction.

[0062] The generation unit learns from past song data and lyric data. For example, the generation unit uses past song data and lyric data to generate lyrics and melodies that match a specified theme or mood. For example, the generation unit's generation AI learns from past song data and generates lyrics and melodies based on a specified theme or mood. Alternatively, the generation unit can learn from past lyric data and generate lyrics and melodies based on a specified theme or mood. This allows the generation unit to generate more accurate lyrics and melodies by learning from past data. Some or all of the above processing in the generation unit may be performed using the generation AI, or it may be performed without the generation AI. For example, the generation unit can input past song data and lyric data into the generation AI, and the generation AI can generate lyrics and melodies based on the data it has learned.

[0063] The generation unit generates lyrics and melodies based on a specified theme or mood. For example, the generation unit generates lyrics and melodies based on a theme or mood specified by the user. For example, the generation unit's generation AI generates bright and cheerful lyrics and melodies based on the theme of "fun summer memories." The generation unit can also have its generation AI generate lyrics and melodies with the theme of "love." In this way, the generation unit can generate lyrics and melodies that match the specified theme or mood. Some or all of the above processing in the generation unit may be performed using the generation AI, or it may be performed without using the generation AI. For example, the generation unit can input a specified theme or mood into the generation AI, and the generation AI can generate lyrics and melodies.

[0064] The providing unit can review the lyrics and melody generated by the user and make corrections or adjustments. For example, the providing unit can review the lyrics and melody generated by the user and make corrections or adjustments as needed. For example, the providing unit can change parts of the generated lyrics or adjust the tempo of the melody. The providing unit can also provide the generated lyrics and melody to the user and make corrections or adjustments after the user has reviewed them. This allows the providing unit to review the generated lyrics and melody and make corrections or adjustments as needed. Some or all of the above processing in the providing unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the providing unit can input the generated lyrics and melody into a generation AI, which can then make corrections or adjustments.

[0065] The reception desk can estimate the user's emotions and suggest themes and moods based on those estimated emotions. For example, if the user is feeling stressed, the reception desk can suggest relaxing themes and moods. If the user is feeling happy, the reception desk can suggest cheerful themes and moods. Furthermore, if the user is feeling sad, the reception desk can suggest comforting themes and moods. In this way, the reception desk can suggest appropriate themes and moods based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using generative AI or not. For example, the reception desk can input user emotion data into a generative AI, which can estimate the emotion and suggest themes and moods.

[0066] The reception desk can analyze the user's past theme and mood selection history and automatically suggest the most suitable theme and mood. For example, the reception desk can suggest similar themes and moods based on themes and moods the user has selected in the past. It can also suggest seasonally appropriate themes and moods based on themes and moods the user has selected during a particular season. Furthermore, the reception desk can suggest themes and moods suitable for a specific time of day based on the user's past selection history. This allows the reception desk to suggest the most suitable theme and mood based on the user's past selection history. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception desk can input the user's past selection history data into a generative AI, which can then suggest the most suitable theme and mood.

[0067] The reception desk can automatically complete keywords related to the theme or mood specified by the user. For example, if the user specifies "love" as the theme, the reception desk can automatically complete related keywords such as "romance," "passion," and "bond." Similarly, if the user specifies "summer" as the theme, the reception desk can automatically complete related keywords such as "beach," "sun," and "vacation." Furthermore, if the user specifies "sad" as the mood, the reception desk can automatically complete related keywords such as "tears," "heartbreak," and "loneliness." In this way, the reception desk can set more specific themes and moods by automatically completing keywords related to the theme or mood specified by the user. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception desk can input the theme or mood specified by the user into a generative AI, and the generative AI can automatically complete related keywords.

[0068] The reception desk can estimate the user's emotions and narrow down the theme and mood options based on the estimated emotions. For example, if the user is relaxed, the reception desk will prioritize displaying relaxing themes and moods. If the user is excited, the reception desk can prioritize displaying energetic themes and moods. Furthermore, if the user is tired, the reception desk can prioritize displaying calming themes and moods. In this way, the reception desk can narrow down the appropriate theme and mood options based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using or without generative AI. For example, the reception desk can input user emotion data into a generative AI, which can estimate the emotions and narrow down the theme and mood options.

[0069] The reception desk can suggest region-specific themes and moods, taking into account the user's geographical location. For example, if the user is at the beach, the reception desk can suggest sea-related themes and moods. If the user is in a mountainous area, the reception desk can suggest nature and adventure-related themes and moods. Furthermore, if the user is in an urban area, the reception desk can suggest urban themes and moods. In this way, the reception desk can suggest region-specific themes and moods based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception desk can input the user's geographical location information into a generative AI, which can then suggest region-specific themes and moods.

[0070] The reception desk can analyze a user's social media activity and suggest relevant themes and moods. For example, if a user frequently posts about "travel" on social media, the reception desk can suggest travel-related themes and moods. Similarly, if a user frequently posts about "music," the reception desk can suggest music-related themes and moods. Furthermore, if a user frequently posts about "cooking," the reception desk can suggest cooking-related themes and moods. In this way, the reception desk can suggest relevant themes and moods based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input the user's social media activity data into a generative AI, which can then suggest relevant themes and moods.

[0071] The generation unit can estimate the user's emotions and adjust the tone of the lyrics and melody based on the estimated emotions. For example, if the user is in a happy mood, the generation unit can generate lyrics and melodies with a bright and cheerful tone. If the user is in a sad mood, the generation unit can generate lyrics and melodies with a melancholic tone. Furthermore, if the user is relaxed, the generation unit can generate lyrics and melodies with a calm and soothing tone. In this way, the generation unit can generate more personalized music by adjusting the tone of the lyrics and melody based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user emotion data into the generation AI, which can estimate the emotions and adjust the tone of the lyrics and melody.

[0072] The generation unit can generate more personalized lyrics and melodies by referring to the user's past song generation history during the generation process. For example, the generation unit can generate lyrics and melodies in a similar style based on the style of songs the user has previously generated. It can also generate relevant lyrics and melodies based on themes and moods the user has previously preferred. Furthermore, the generation unit can generate lyrics and melodies influenced by specific artists from the user's past song generation history. In this way, the generation unit can generate more personalized lyrics and melodies based on the user's past song generation history. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's past song generation history data into a generation AI, which can then generate more personalized lyrics and melodies.

[0073] The generation unit can incorporate cultural backgrounds and historical elements related to the specified theme or mood during the generation process. For example, if the user specifies "love" as the theme, the generation unit can generate lyrics and melodies that incorporate elements of historical love songs. Similarly, if the user specifies "summer" as the theme, the generation unit can generate lyrics and melodies that incorporate cultural elements related to summer traditions. Furthermore, if the user specifies "sad" as the mood, the generation unit can generate lyrics and melodies that incorporate elements of tragic stories and poems. In this way, the generation unit can generate more profound lyrics and melodies by incorporating cultural backgrounds and historical elements related to the specified theme or mood. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the specified theme or mood into a generation AI, which can then generate lyrics and melodies that incorporate cultural backgrounds and historical elements.

[0074] The generation unit can estimate the user's emotions and adjust the length of the lyrics and melody based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise lyrics and melodies. If the user is relaxed, the generation unit can generate longer lyrics and melodies that include detailed explanations. Furthermore, if the user is excited, the generation unit can generate lyrics and melodies with visually stimulating effects. In this way, the generation unit can generate more appropriate music by adjusting the length of the lyrics and melody based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without a generation AI. For example, the generation unit can input user emotion data into a generation AI, which can estimate the emotions and adjust the length of the lyrics and melody.

[0075] The generation unit can incorporate region-specific musical styles by considering the user's geographical location during the generation process. For example, if the user is in Latin America, the generation unit can generate lyrics and melodies incorporating elements of Latin music. If the user is in Africa, the generation unit can generate lyrics and melodies incorporating rhythms and melodies of African music. Furthermore, if the user is in Asia, the generation unit can generate lyrics and melodies incorporating elements of traditional Asian music. In this way, the generation unit can generate more locally rooted songs by incorporating region-specific musical styles based on the user's geographical location. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI, which can then generate lyrics and melodies incorporating region-specific musical styles.

[0076] The generation unit can analyze the user's social media activity during generation and generate lyrics and melodies that reflect relevant trends. For example, if the user frequently posts about "travel" on social media, the generation unit can generate travel-related lyrics and melodies. Similarly, if the user frequently posts about "music," the generation unit can generate music-related lyrics and melodies that reflect relevant trends. Furthermore, if the user frequently posts about "cooking," the generation unit can generate cooking-related lyrics and melodies. In this way, the generation unit can generate lyrics and melodies that reflect relevant trends based on the user's social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's social media activity data into a generation AI, which can then generate lyrics and melodies that reflect relevant trends.

[0077] The service provider can estimate the user's emotions and adjust the display method of the lyrics and melody based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a display method that gets straight to the point. In this way, the service provider can provide a more appropriate display method by adjusting the display method of the lyrics and melody based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using the generative AI or not. For example, the service provider can input user emotion data into the generative AI, which can estimate the emotions and adjust the display method.

[0078] The service provider can, at the time of delivery, refer to the user's past revision history to make optimal revision suggestions. For example, the service provider can make similar revision suggestions based on parts the user has previously revised. Furthermore, the service provider can make revision suggestions that match a specific style based on the user's revision history. In addition, the service provider can make revision suggestions influenced by a specific artist based on the user's past revision history. This allows the service provider to make optimal revision suggestions based on the user's past revision history. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input the user's past revision history data into a generative AI, which can then make optimal revision suggestions.

[0079] The service provider can add a function to collect user feedback in real time during the service provision process and reflect it in the next generation. For example, the service provider can provide an interface that allows users to provide feedback in real time. The service provider can also introduce an algorithm that reflects user feedback in the next generation. Furthermore, the service provider can build a system that collects user feedback in real time and reflects it in the generation AI. This allows the service provider to collect user feedback in real time and reflect it in the next generation, thereby enabling the creation of music that better meets user needs. Some or all of the above processing in the service provider may be performed using the generation AI or not. For example, the service provider can input user feedback data into the generation AI, which can then reflect it in the next generation.

[0080] The service provider can estimate the user's emotions and determine the priority of lyrics and melodies to offer based on the estimated emotions. For example, if the user is tense, the service provider may prioritize providing relaxing lyrics and melodies. If the user is relaxed, the service provider may prioritize providing lyrics and melodies that contain detailed information. Furthermore, if the user is in a hurry, the service provider may prioritize providing lyrics and melodies that get straight to the point. In this way, the service provider can provide more appropriate music by determining the priority of lyrics and melodies to offer based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input user emotion data into a generative AI, which can estimate the emotions and determine the priority.

[0081] The service provider can, at the time of delivery, consider the user's geographical location and make region-specific modification suggestions. For example, if the user is at the beach, the service provider can make modification suggestions related to the sea. If the user is in a mountainous area, the service provider can make modification suggestions related to nature or adventure. Furthermore, if the user is in an urban area, the service provider can make urban modification suggestions. In this way, the service provider can make more region-specific modifications by making region-specific modification suggestions based on the user's geographical location. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input the user's geographical location information into a generative AI, and the generative AI can make region-specific modification suggestions.

[0082] The service provider can analyze the user's social media activity at the time of delivery and make correction suggestions that reflect relevant feedback. For example, if the user frequently posts about "travel" on social media, the service provider can make travel-related correction suggestions. Similarly, if the user frequently posts about "music," the service provider can make music-related correction suggestions. Furthermore, if the user frequently posts about "cooking," the service provider can make cooking-related correction suggestions. This allows the service provider to make more appropriate corrections by making correction suggestions that reflect relevant feedback based on the user's social media activity. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without a generative AI. For example, the service provider can input the user's social media activity data into a generative AI, which can then make correction suggestions that reflect relevant feedback.

[0083] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0084] The reception desk can automatically suggest relevant images and videos based on the theme or mood specified by the user. For example, if the user specifies the theme "fun summer memories," the reception desk will suggest images and videos of beaches and summer festivals. If the user specifies the theme "love," it can suggest images and videos of romantic scenes. Furthermore, if the user specifies the mood "sad," it can suggest touching movie scenes or poetic images. In this way, the reception desk can provide visual content related to the theme or mood specified by the user, allowing them to form a more concrete image.

[0085] The generation unit can estimate the user's emotions and adjust the tempo of the lyrics and melody based on those emotions. For example, if the user is excited, it can generate lyrics and melodies with a fast tempo. If the user is relaxed, it can generate lyrics and melodies with a slow tempo. Furthermore, if the user is feeling sad, it can generate lyrics and melancholic, slow-tempo lyrics and melody. In this way, the generation unit can create songs that better match the user's emotions by adjusting the tempo of the lyrics and melody based on those emotions.

[0086] The service provider can provide a function that allows users to receive real-time feedback from other users when reviewing the lyrics and melody they have generated. For example, a user can share a song they have generated and receive comments and ratings from other users in real time. The service provider can also suggest revisions and adjustments to the song based on the feedback from other users. Furthermore, the service provider can compile the feedback and present the revision that received the most support from users. This allows the service provider to help users improve their songs while receiving feedback from other users.

[0087] The generation unit can estimate the user's emotions and select the genre of lyrics and melody based on those emotions. For example, if the user is feeling happy, it can select pop or dance music genres. If the user is feeling sad, it can select ballad or blues genres. Furthermore, if the user is relaxed, it can select jazz or classical genres. In this way, the generation unit can provide songs that better match the user's emotions by generating lyrics and melodies in the appropriate genre based on the user's feelings.

[0088] The reception desk can automatically suggest relevant literary works and poems based on themes and moods specified by the user. For example, if the user specifies "love" as their theme, it can suggest Shakespearean sonnets and romantic poems. If the user specifies "sad" as their mood, it can suggest moving poems and stories. Furthermore, if the user specifies "adventure" as their theme, it can suggest adventure novels and poems. In this way, the reception desk can gain deeper inspiration by providing literary works and poems related to the themes and moods specified by the user.

[0089] The generation unit can estimate the user's emotions and adjust the instrumentation of the lyrics and melody based on those emotions. For example, if the user is excited, it can generate an arrangement that heavily utilizes electric guitar and drums. If the user is relaxed, it can generate an arrangement centered on acoustic guitar and piano. Furthermore, if the user is feeling sad, it can generate a melancholic arrangement using violin and cello. In this way, the generation unit can create songs that better match the user's emotions by adjusting the instrumentation based on their feelings.

[0090] The service provider can include a function to add visual effects when users review the generated lyrics and melody. For example, if a user specifies a cheerful theme, bright colors and dynamic effects can be added. If a user specifies a sad mood, calm colors and quiet effects can be added. Furthermore, if a user specifies a relaxed mood, gentle colors and mellow effects can be added. This allows the service provider to visually enjoy the music generated by the user.

[0091] The reception desk can automatically suggest relevant artworks and photographs based on the theme or mood specified by the user. For example, if the user specifies "love" as their theme, it will suggest romantic paintings and photographs. If the user specifies "sadness" as their mood, it can suggest emotionally moving artworks and photographs. Furthermore, if the user specifies "adventure" as their theme, it can suggest artworks and photographs depicting adventurous scenes. In this way, the reception desk can provide artworks and photographs related to the theme or mood specified by the user, allowing them to form a more concrete image.

[0092] The generation unit can estimate the user's emotions and adjust the key of the lyrics and melody based on those emotions. For example, if the user is in a happy mood, it can generate lyrics and melody in a bright key. If the user is in a sad mood, it can generate lyrics and melody in a dark key. Furthermore, if the user is relaxed, it can generate lyrics and melody in a calm key. In this way, the generation unit can create songs that better match the user's emotions by adjusting the key based on those emotions.

[0093] The service provider can include a feature that uses a voice assistant to guide users as they review the generated lyrics and melody. For example, if a user wants to change part of a song, the voice assistant can guide them through the specific steps of making the change. Similarly, if a user wants to adjust the tempo of a melody, the voice assistant can suggest an appropriate tempo. Furthermore, if a user wants to revise part of the lyrics, the voice assistant can offer revision suggestions. This allows the service provider to enable users to easily revise songs with the guidance of a voice assistant.

[0094] The following briefly describes the processing flow for example form 2.

[0095] Step 1: The reception desk allows users to specify a theme or mood. Users can enter themes or moods such as "fun," "sad," or "love." For example, they can specify the theme "fun summer memories." Step 2: The generation unit analyzes the theme and mood specified by the reception unit and generates lyrics and melody. The generation unit uses a generation AI to learn from past song data and lyric data and generates lyrics and melody that match the specified theme and mood. For example, based on the theme "fun summer memories," it generates bright and cheerful lyrics and melody. Step 3: The providing unit provides the user with the lyrics and melody generated by the generating unit. The user can review the generated lyrics and melody and make corrections or adjustments as needed. For example, they can change part of the lyrics or adjust the tempo of the melody.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0098] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0099] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, allowing the user to input a theme or mood. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates lyrics and melodies using a generation AI. The provision unit is implemented, for example, by the output device 40 of the smart device 14, which provides the generated lyrics and melody to the user. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0100] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0101] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0103] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0104] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0106] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0107] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0108] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0109] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0110] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0111] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0114] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing the user to input a theme or mood by voice. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, generating lyrics and melodies using a generation AI. The provision unit is implemented by the speaker 240 of the smart glasses 214, providing the generated lyrics and melody to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0116] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0117] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, allowing the user to input a theme or mood by voice. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates lyrics and melodies using a generation AI. The provision unit is implemented by, for example, the display 343 of the headset terminal 314, which provides the generated lyrics and melody to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0132] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0133] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0140] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, allowing the user to input a theme or mood by voice. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates lyrics and melodies using a generation AI. The provision unit is implemented by, for example, the speaker 240 of the robot 414, which provides the generated lyrics and melody to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0149] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0151] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0152] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0153] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0157] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0158] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0159] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0160] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0161] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0162] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0164] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0165] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0166] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0167] (Note 1) A reception desk where users specify themes and moods, A generation unit analyzes the theme and mood specified by the reception unit and generates lyrics and melody, The system includes a providing unit that provides the user with lyrics and melodies generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is It is learning from past song data and lyric data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generates lyrics and melodies based on a specified theme or mood. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Users can review, correct, and adjust the generated lyrics and melody. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and suggests themes and moods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It analyzes the user's past theme and mood selection history and automatically suggests the most suitable theme and mood. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Automatically completes keywords related to the theme or mood specified by the user. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and narrows down the theme and mood options based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is We propose themes and moods specific to the region, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is We analyze users' social media activity and suggest relevant themes and moods. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is It estimates the user's emotions and adjusts the tone of the lyrics and melody based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is During generation, the system references the user's past song generation history to create more personalized lyrics and melodies. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During generation, cultural backgrounds and historical elements related to the specified theme or mood are incorporated. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and adjusts the length of the lyrics and melody based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, the system takes into account the user's geographical location and incorporates region-specific musical styles. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, the system analyzes the user's social media activity and generates lyrics and melodies that reflect relevant trends. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the lyrics and melody are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing the update, we refer to the user's past revision history to suggest the most suitable revisions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, We will add a feature to collect user feedback in real time upon release and incorporate it into future generation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the lyrics and melody to be provided based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, we will consider the user's geographical location and suggest region-specific modifications. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, At the time of release, we analyze users' social media activity and propose modifications that reflect relevant feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where users specify themes and moods, A generation unit analyzes the theme and mood specified by the reception unit and generates lyrics and melody, The system includes a providing unit that provides the user with lyrics and melodies generated by the generation unit. A system characterized by the following features.

2. The generating unit is It is learning from past song data and lyric data. The system according to feature 1.

3. The generating unit is Generates lyrics and melodies based on a specified theme or mood. The system according to feature 1.

4. The aforementioned supply unit is, Users can review, correct, and adjust the generated lyrics and melody. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and suggests themes and moods based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is It analyzes the user's past theme and mood selection history and automatically suggests the most suitable theme and mood. The system according to feature 1.

7. The aforementioned reception unit is Automatically completes keywords related to the theme or mood specified by the user. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and narrows down the theme and mood options based on those estimated emotions. The system according to feature 1.

Images